Recently, deep learning-based methods [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]} have been successfully applied to LF image SR, and achieved promising performance. Zhang et al. [4]} decoded the LF structural information into four stacked SAIs along different angular directions, and super-resolved the center image using a multi-branch residual network. Wang et al. [6]} proposed an LF-InterNet to decouple and interact spatial and angular information for LF image SR. Meng et al. [5]} proposed a 4D convolutional network to handle the LF structural information, which fully exploits the LF information from all adjacent SAIs. Jin et al. [7]} proposed an all-to-one (ATO) framework to learn the structural information and proposed a structural consistency regularization approach. Due to the occlusions and non-Lambertian reflections in LFs, the information in different views and different channels is of different importance. Existing learning-based LF image SR methods [4]}, [6]}, [7]}, [15]} treat the view-wise and channel-wise features equally for LF image SR, and cannot discriminatively use the informative information in an LF for further performance improvement.
